Summary

What You'll Do

• Orchestrate AI agents (Claude, Cursor, custom MCP tools) to design and deliver backend systems and data services
• Decompose complex engineering projects into AI-executable tasks, manage workflows, and ensure quality outcomes
• Review and validate AI-generated code for correctness, performance, and maintainability
• Design system architectures that AI agents will implement—you set the vision, AI does the execution
• Build and refine AI workflows for common engineering patterns (data extraction, API development, testing)
• Collaborate with AI-assisted teams to establish best practices for AI-driven development
• Own outcomes, not code—you're responsible for working systems, not personally writing every line

A Day in the Life

You're tasked with building a new data extraction service. Instead of opening your editor:

1. You design the architecture—database schema, API contracts, error handling strategy by conversing with a CTO AI
2. You write specifications for AI agents—clear requirements, edge cases, quality criteria
3. You orchestrate AI to implement the service—using Cursor/Claude to generate code, tests, and documentation
4. You review and validate—run tests, check edge cases, ensure it meets production standards
5. You iterate with AI—refine, optimize, and handle any issues that arise

You shipped a production-ready service in a day or two that might have taken a week of hands-on coding. That's AI-forward engineering.

What This Role Is (and Isn't)

This is AI-forward engineering. You'll spend more time designing, directing, and validating than writing code from scratch. You'll work through AI agents to deliver systems at scale and velocity that wouldn't be possible alone.

This is not traditional software engineering. If you love being in the editor all day writing every line yourself, this isn't the role. If the idea of delegating implementation to AI makes you uncomfortable, we're not there yet together.

This requires strong engineering judgment. You need to know what good looks like—solid architecture, clean abstractions, proper error handling, testability. AI will do the work, but you're the one ensuring quality.

You Might Be a Fit If You...

• Have 3 years of software engineering experience and understand backend systems deeply (even if you won't write all the code)
• Are already working effectively with AI coding assistants (Cursor, Claude, GitHub Copilot, etc.)
• Can think architecturally—you know what good systems look like and can guide AI to build them
• Are comfortable validating code you didn't write—you can review AI output for correctness, performance, and edge cases
• Can decompose complex problems into clear specifications that AI agents can execute
• Trust but verify—you believe AI can do the work, but you know when and how to intervene
• Thrive in ambiguity and rapid iteration—this is a new way of working, and we're figuring it out together